ABI Development: PathBubbles for Dynamic Visualization and Integration of Biological Information

现代生物学的许多学科在数据获取方面经历了一场革命。随着高吞吐量技术的出现，数据积累的速度超过了我们将数据转化为知识的能力。这些技术的应用可以提供与特定生物问题相关的万亿字节的数据，但解释如此大量的信息仍然是一个挑战。有各种各样的资源可以帮助研究人员可视化、分类并最终从他们的数据中获得意义。可视化工具，如KEGG或Reactome中的工具，将数据放在信号和代谢路径的上下文中。已经开发了许多不同的本体、文本挖掘和丰富分析工具来帮助将单个数据点分类到组中。可视化和分类都降低了问题的复杂性，并提供了对潜在生物学的洞察。然而，归根结底，人们仍然需要必要的步骤来整合、评估并最终将这些数据转化为人类知识。需要一种新颖、动态的途径可视化方法，同时整合不同的本体和文本中的信息来提高研究人员的水平？S能够将高通量数据转化为理解。这将通过开发路径气泡来实现，这是一个动态、交互的路径可视化工具，使用现有的VIS-和代码气泡作为框架。此外，还将通过整合在特定本体、文本挖掘工具和表达数据中发现的数据来提供额外的信息，以提供用于PathBubble的基因注释。最后，从文献中捕获有关翻译后修饰蛋白质的功能信息并将这些信息整合到PathBubble中，将有助于用户开发可测试的假说。人类是视觉动物，依靠视觉输入来感知和定位自己对环境的适应。这样做的一个结果是，人类非常能够识别视觉显示信息中的模式。这项工作利用这一能力，通过开发一种新的基于网络的界面，将信息显示为图形，帮助生物学家分析数千个路径数据点。这张图将显示来自基因研究的数据，其中每个基因产物用一个点表示，基因之间的联系用线表示。代表基因产物的圆点可以根据基因在特定生物条件下的活性而着色。例如，如果与正常细胞相比，该基因在癌细胞中的表达水平非常高，则该点将显示为红色。此外，这些线条可以代表各种相互作用，例如基因产物之间的结合或共享一个小分子，相互作用的类型可以用不同的线条颜色来表示。图形界面得到了关于每个基因产物和每个相互作用的信息的广泛数据库的支持。用户只需点击感兴趣的点或线即可访问该信息。该项目的一个特别新颖的方面是，用户将能够通过使用允许他们创建新的点(基因产物)和线(相互作用)的界面来添加他们自己的数据。然后，他们将能够提供关于发生什么的功能性信息，例如，当他们的基因产品与数据库中已有的基因产品相互作用时。根据这一新信息，该系统将预测用户的新基因产物对生物途径的影响。这将允许用户在进行实际实验之前使用该界面来测试假设，从而提出“如果”的问题。虽然该系统是在生物学的背景下开发的，但以图形测试不同假设的能力将应用于包括化学、工程、物理和计算机科学在内的各种其他学科。

Many disciplines of modern biology have undergone a revolution in data acquisition. With the advent of high throughput technologies, data is accumulating at a pace that outstrips our ability to convert that data into knowledge. Application of these technologies can provide terabyte amounts of data relevant to a particular biological problem but interpreting that volume of information remains a challenge. A variety of resources are available to help researchers visualize, categorize and ultimately make sense out of their data. Visualization tools such as those in KEGG or Reactome, place data in the context of signaling and metabolic pathways. Many different ontologies, text mining and enrichment analysis tools have been developed to help categorize individual data points into groups. Both visualization and categorization reduce the complexity of the problem and provide insight into the underlying biology. Ultimately, however, people are still in need for the essential steps of integrating, evaluating and, finally, converting these data to human knowledge. What is needed a novel, dynamic approach to pathway visualization along with integrating disparate ontologies and information found in text to improve the researcher?s ability to convert high throughput data into understanding. This will be achieved by developing PathBubbles, a dynamic, interactive pathway visualization tool using the existing Vis- and Code Bubbles as a framework. In addition additional information will be provided by integrating data found in specific ontologies, text-mining tools and expression data to provide gene annotation for use with PathBubbles. Finally, capturing functional information about post-translationally modified proteins from literature and integrating this information into PathBubbles, will assist users in developing testable hypotheses. Humans are visual animals, relying on visual input to sense and orient themselves to the environment. One consequence of this is that humans are very able to recognize patterns in visually displayed information. This work exploits this ability to help biologists analyze thousands of pathway data points by developing a novel web based interface where information is displayed as a graph. This graph will display data from gene studies where each gene product is shown as a dot and the connections between the genes are lines. The dots that represent gene products can be colored depending on the activity of the gene in a particular biological condition. For example, if the gene is expressed at a very high level in a cancer cell compared to a normal cell, the dot will be displayed in red. In addition, the lines may represent a variety of interactions such as binding between gene products or sharing of a small molecule and the type of interaction can be indicated by different line colors. The graphical interface is supported by an extensive database of information about each gene product and each interaction. Users will be able to access that information by simply clicking on the dot or line of interest. A particularly novel aspect of this project is that users will be able to add their own data by using an interface that allows them to create new dots (gene products) and lines (interactions). They will then be able to provide functional information about what happens, for instance, when their gene product interacts with a pre-existing gene product already in the database. Based on this new information, the system will then predict the effect of the user's new gene product on the biological pathways. This will allow users to ask 'what if' questions, using this interface to test hypotheses before doing actual experiments. While the system is being developed in the context of biology, the ability to graphically test different hypotheses will have application to a variety of other disciplines including chemistry, engineering, physics and computer sciences.